A survey on node discovery in Mobile Internet of Things(IoT) scenarios

Valarmathi, M. L.; Sumathi, L.; Deepika, G

doi:10.1109/icaccs.2016.7586400

Cited by 7 publications

(8 citation statements)

References 11 publications

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“…Resource discovery could be limited by a time interval in the processing of data for disaster monitoring [34]. Furthermore, to support loT application to provide rich ubiquitous connectivity, the elements of '6A' connectivity are required (Anytime, Anyplace, Any service, Anything, Anyone, and Any path) [35].…”

Section: Resources Dsicovery For Iiotmentioning

confidence: 99%

“…In the meantime, in [35] researcher in the resource management field said, resource discovery within the mobile IoT network can be classified into different scenarios as follows:…”

Section: Resources Dsicovery For Iiotmentioning

confidence: 99%

“…Ontology-based Approach In summary, the techniques discussed in the field of IoT resource discovery has been refined to clarify their performance for future use. We list in Table 2 each of the related techniques and compare their main advantage and main disadvantage as a guide for future researchers [35] Battery saving.…”

Section: Resources Dsicovery For Iiotmentioning

confidence: 99%

“…We see there are two prominent techniques in current IIoT data searching, Context-Aware Resource Discovery and Context-Based Search Techniques. Context-Aware Resource Discovery under Learning Based Approach offer Q-learning as intended by [35] works by verifying an action-value function based on the interactions of the controller with the environment [39]. Q-learning is the latest Context-Aware Resource Discovery framework to make node discovery.…”

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confidence: 99%

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Resource Discovery in High-Volume Internet of Things: Systematic Research

2019

IJITEE

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Currently, several issues in Internet of Things (IoT) resource discovery is still being analyzed and verified. This paper offers a comparison with systematic literature and solution perspective on the resources discovery issues in the high-volume IoT environment. The purpose is to recommend a clear review on how the resources could be maximize or improve properly within different IoT solutions. We compared and reviewed all IoT resource discovery literature and solutions, categorizing a final collection of 19 related papers and then produce summarisation in three different tables. Based on the outputs, we are able to provide an overview of the IoT resource discovery categories, main areas of the research, IoT platforms, simulation tools and recommended solutions. Finally, we hope this review can provides insight result for every IoT sources, propose appropriate techniques or methods that can help to ensure the IoT resource discovery becomes most effective in the future..

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Section: Resources Dsicovery For Iiotmentioning

confidence: 99%

“…In the meantime, in [35] researcher in the resource management field said, resource discovery within the mobile IoT network can be classified into different scenarios as follows:…”

Section: Resources Dsicovery For Iiotmentioning

confidence: 99%

Section: Resources Dsicovery For Iiotmentioning

confidence: 99%

mentioning

confidence: 99%

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Resource Discovery in High-Volume Internet of Things: Systematic Research

2019

IJITEE

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“…The same tasks are also applicable for data transmission between the three layers—from the end devices to the remote server or cloud. Various popular solutions to node discovery in IoT include time synchronized protocols (e.g., recursive binary tree partitioning, wakeup scheduling), deterministic approaches (e.g., Searchlight), probabilistic approaches (e.g.,Bloom filters [Dautov, Distefano, Senko, & Surnin, ]), colocation‐based approaches (e.g., randomized discovery, context‐aware power management discovery), fully distributed opportunistic approaches (e.g., efficient application‐layer discovery protocol), and learning‐based approaches (e.g., Q‐learning) (Valarmathi, Sumathi, & Deepika, ).…”

Section: Knowledge Discovery For Iotmentioning

confidence: 99%

Knowledge discovery for enabling smart Internet of Things: A survey

Misra¹,

Mukherjee²,

Roy

2018

WIREs Data Min & Knowl

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The use of knowledge discovery on the Internet of Things (IoT) and its allied domains is undeniably one of the most indispensable ones, which results in optimized placement architectures, efficient routing protocols, device energy savings, and enhanced security measures for the implementation. The absence of knowledge discovery in IoT results in just an implementation of large‐scale sensor networks, which generates a huge amount of data, and which needs, an often under‐optimized, processing for actionable outputs. In this survey, we explore various domains of IoT for which knowledge discovery is inseparable from the application, and show how it benefits the overall implementation of the IoT architecture. This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Machine Learning Application Areas > Internet and Web‐Based Applications Algorithmic Development > Spatial and Temporal Data Mining

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Internet of Things Business Models: The RAWFIE Case

Papadopoulou

Kolomvatsos

Hadjiefthymiades

2019

Lecture Notes in Computer Science

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Internet of Things (IoT) encompasses a wide range of devices and technologies which cumulatively shape a new environment with unprecedented business prospects. This paper aims to investigate the business potential of IoT, focusing on mobile IoT and IoT as a service. In this direction, it presents the case of RAWFIE, a H2020 project on a research and experimentation federated platform of mobile IoT testbeds and devices. The paper describes RAWFIE potential business models, analyzing them into their characteristics, based on the business model canvas, examining the possibilities as well as the challenges these entail, using a SWOT analysis, and testing them in a preliminary evaluation. The study offers research and practical insights as a starting point for business models of IoT as a service, focusing in the context of mobile IoT experimentation.

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A survey on node discovery in Mobile Internet of Things(IoT) scenarios

Cited by 7 publications

References 11 publications

Resource Discovery in High-Volume Internet of Things: Systematic Research

Resource Discovery in High-Volume Internet of Things: Systematic Research

Knowledge discovery for enabling smart Internet of Things: A survey

Internet of Things Business Models: The RAWFIE Case

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